MTL is best suited to situations where it is possible to train with all data from scratch. However, in some cases an existing model needs to be adapted to a new situation.
Transfer learning provides a framework from which to address this problem.
The most common approach to transfer learning is to train a model in one domain and fine-tune it in a related domain [1]}, [2]}, [3]}. This pre-training and fine-tuning (PT/FT) approach has been successful in cases where the size of available data for the source domain is abundant in comparison to size of the data in the target domain. For example, in ASR, transfer learning is used to transfer knowledge from a richly-resourced language to an under-resourced language [1]}. In emotion recognition, Deng et al. [2]} presented a sparse auto-encoder method for transferring knowledge between six emotion recognition datasets. The authors used auto-encoders to transform a source domain and its features to a domain that is more consistent with the target domain. The authors then used the transformed source domain to learn SVM classifiers to predict emotions in the target domain. Ng et al. [3]} studied transfer learning in the context of facial expression recognition. The authors first trained a model on a general large-scale dataset (ImageNet) that contains \(1.2\)  million images. They then fine-tuned the trained model on four datasets that contain relevant facial expressions similar to those in the target domain. Finally, the authors fine-tuned the network using the target data. The authors found that the two-step fine tuning approach provided improvements over one-step fine-tuning and no fine-tuning.
